{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import polars as pl\n",
    "from datetime import datetime\n",
    "import gc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "full_data = pd.read_csv('stb_equip_wheel_log.csv', encoding='gbk')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "full_data['ts'] = pd.to_datetime(full_data['ts'])\n",
    "full_data['date'] = full_data['ts'].dt.strftime('%Y-%m-%d')\n",
    "full_data['hour'] = full_data['ts'].dt.hour\n",
    "full_data['month'] = full_data['ts'].dt.month\n",
    "full_data['time'] = full_data['ts'].dt.strftime('%Y-%m-%d %H:%M:%S')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ts</th>\n",
       "      <th>temp_f_l</th>\n",
       "      <th>temp_f_r</th>\n",
       "      <th>temp_m_l</th>\n",
       "      <th>temp_m_r</th>\n",
       "      <th>temp_b_l</th>\n",
       "      <th>temp_b_r</th>\n",
       "      <th>pressure_f_l</th>\n",
       "      <th>pressure_f_r</th>\n",
       "      <th>pressure_m_l</th>\n",
       "      <th>...</th>\n",
       "      <th>pressure_b_l</th>\n",
       "      <th>pressure_b_r</th>\n",
       "      <th>tag_apply_category_code</th>\n",
       "      <th>tag_equipment_type_code</th>\n",
       "      <th>tag_equipment_id</th>\n",
       "      <th>date</th>\n",
       "      <th>hour</th>\n",
       "      <th>month</th>\n",
       "      <th>time</th>\n",
       "      <th>vehicle_number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2024-05-23 15:20:29.670</td>\n",
       "      <td>18.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>780.0</td>\n",
       "      <td>780.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>810.0</td>\n",
       "      <td>840.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>162</td>\n",
       "      <td>2024-05-23</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>2024-05-23 15:20:29</td>\n",
       "      <td>Z10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2024-05-23 15:20:30.639</td>\n",
       "      <td>18.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>780.0</td>\n",
       "      <td>780.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>810.0</td>\n",
       "      <td>840.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>162</td>\n",
       "      <td>2024-05-23</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>2024-05-23 15:20:30</td>\n",
       "      <td>Z10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2024-05-23 15:20:31.638</td>\n",
       "      <td>18.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>780.0</td>\n",
       "      <td>780.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>810.0</td>\n",
       "      <td>840.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>162</td>\n",
       "      <td>2024-05-23</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>2024-05-23 15:20:31</td>\n",
       "      <td>Z10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2024-05-23 15:20:32.637</td>\n",
       "      <td>18.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>780.0</td>\n",
       "      <td>780.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>810.0</td>\n",
       "      <td>840.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>162</td>\n",
       "      <td>2024-05-23</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>2024-05-23 15:20:32</td>\n",
       "      <td>Z10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2024-05-23 15:20:33.889</td>\n",
       "      <td>18.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>780.0</td>\n",
       "      <td>780.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>810.0</td>\n",
       "      <td>840.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>162</td>\n",
       "      <td>2024-05-23</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>2024-05-23 15:20:33</td>\n",
       "      <td>Z10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       ts  temp_f_l  temp_f_r  temp_m_l  temp_m_r  temp_b_l  \\\n",
       "0 2024-05-23 15:20:29.670      18.0      19.0       NaN       NaN      24.0   \n",
       "1 2024-05-23 15:20:30.639      18.0      19.0       NaN       NaN      24.0   \n",
       "2 2024-05-23 15:20:31.638      18.0      19.0       NaN       NaN      24.0   \n",
       "3 2024-05-23 15:20:32.637      18.0      19.0       NaN       NaN      24.0   \n",
       "4 2024-05-23 15:20:33.889      18.0      19.0       NaN       NaN      24.0   \n",
       "\n",
       "   temp_b_r  pressure_f_l  pressure_f_r  pressure_m_l  ...  pressure_b_l  \\\n",
       "0      22.0         780.0         780.0           NaN  ...         810.0   \n",
       "1      22.0         780.0         780.0           NaN  ...         810.0   \n",
       "2      22.0         780.0         780.0           NaN  ...         810.0   \n",
       "3      22.0         780.0         780.0           NaN  ...         810.0   \n",
       "4      22.0         780.0         780.0           NaN  ...         810.0   \n",
       "\n",
       "   pressure_b_r  tag_apply_category_code  tag_equipment_type_code  \\\n",
       "0         840.0                        1                        1   \n",
       "1         840.0                        1                        1   \n",
       "2         840.0                        1                        1   \n",
       "3         840.0                        1                        1   \n",
       "4         840.0                        1                        1   \n",
       "\n",
       "   tag_equipment_id        date hour  month                 time  \\\n",
       "0               162  2024-05-23   15      5  2024-05-23 15:20:29   \n",
       "1               162  2024-05-23   15      5  2024-05-23 15:20:30   \n",
       "2               162  2024-05-23   15      5  2024-05-23 15:20:31   \n",
       "3               162  2024-05-23   15      5  2024-05-23 15:20:32   \n",
       "4               162  2024-05-23   15      5  2024-05-23 15:20:33   \n",
       "\n",
       "  vehicle_number  \n",
       "0            Z10  \n",
       "1            Z10  \n",
       "2            Z10  \n",
       "3            Z10  \n",
       "4            Z10  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_name = pd.read_excel('input/庆华项目配置信息.xlsx')\n",
    "car_name.columns = ['tag_equipment_id', 'vehicle_number']\n",
    "full_data = pd.merge(full_data, car_name, how='left', on='tag_equipment_id')\n",
    "del car_name\n",
    "gc.collect()\n",
    "full_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "按秒划分重复的记录值个数为0\n"
     ]
    }
   ],
   "source": [
    "full_data.drop_duplicates(subset=['time', 'vehicle_number'], inplace=True)\n",
    "full_data.reset_index(drop=True, inplace=True)\n",
    "print(f'按秒划分重复的记录值个数为{full_data.duplicated(subset=['time', 'vehicle_number']).sum()}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "col_use = ['temp_f_l', 'temp_f_r', 'temp_m_l', 'temp_m_r', 'temp_b_l','temp_b_r','pressure_f_l', 'pressure_f_r', 'pressure_m_l', 'pressure_m_r', 'pressure_b_l', 'pressure_b_r', 'date', 'hour', 'month', 'time', 'vehicle_number']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt = full_data[col_use].copy()\n",
    "del full_data\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 选取Z02车"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt = dt.loc[dt['vehicle_number']=='Z02']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly_express as px\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "plt.rcParams['figure.figsize'] = [6, 4]\n",
    "plt.rcParams['figure.autolayout'] = True\n",
    "plt.rcParams['font.family'] = 'SimHei'\n",
    "plt.rcParams['axes.unicode_minus'] = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-5.0, 65.0)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tire_temp_related = ['temp_f_l', 'temp_f_r',  'temp_b_l',\n",
    "       'temp_b_r'] #此处可加减使用的轮胎\n",
    "dt_long = pd.melt(dt[tire_temp_related], var_name='variable', value_name='value')\n",
    "plt.figure(figsize=(10,6))\n",
    "\n",
    "sns.violinplot(hue = 'variable',y='value', data=dt_long, palette='Set2')\n",
    "plt.title('Z02车辆每个轮胎的胎温分布图')\n",
    "plt.ylim(-5, 65)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(700.0, 1000.0)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tire_pressure_related = [ 'pressure_f_l', 'pressure_f_r', 'pressure_b_l', 'pressure_b_r']\n",
    "dt_long = pd.melt(dt[tire_pressure_related], var_name='variable', value_name='value')\n",
    "plt.figure(figsize=(10,6))\n",
    "\n",
    "sns.violinplot(hue = 'variable',y='value', data=dt_long, palette='Set1')\n",
    "plt.title('Z02车辆每个轮胎的胎压分布图')\n",
    "plt.ylim(700, 1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 对胎温，胎压建立预警系统"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 胎温"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "temp_f_l_error\n",
       "低温故障          825\n",
       "正常        2885701\n",
       "高温黄色报警        583\n",
       "高温红色报警          0\n",
       "高温故障        83643\n",
       "Name: count, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "temp_f_r_error\n",
       "低温故障          677\n",
       "正常        2889484\n",
       "高温黄色报警       3005\n",
       "高温红色报警          0\n",
       "高温故障        77586\n",
       "Name: count, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "temp_b_l_error\n",
       "低温故障          879\n",
       "正常        2747239\n",
       "高温黄色报警      95248\n",
       "高温红色报警       2738\n",
       "高温故障       124648\n",
       "Name: count, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "temp_b_r_error\n",
       "低温故障          876\n",
       "正常        2468244\n",
       "高温黄色报警     193008\n",
       "高温红色报警      21009\n",
       "高温故障       287615\n",
       "Name: count, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tire_temp_related = ['temp_f_l', 'temp_f_r',  'temp_b_l',\n",
    "       'temp_b_r'] #此处可加减使用的轮胎\n",
    "\n",
    "\n",
    "for col in tire_temp_related:\n",
    "    bins = [-float('inf'), 1, 43, 48, 200, float('inf')] #此处可根据不同的轮胎设置不同的预警划分\n",
    "    labels = ['低温故障', '正常', '高温黄色报警', '高温红色报警', '高温故障'] \n",
    "\n",
    "    dt[f'{col}_error'] = pd.cut(dt[col], bins= bins, labels=labels)\n",
    "    display(dt[f'{col}_error'].value_counts().sort_index())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 胎压 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pressure_f_l_error\n",
       "低压故障        37438\n",
       "低压红色报警        395\n",
       "低压黄色报警      48173\n",
       "正常        2643581\n",
       "高压黄色报警      31358\n",
       "高压红色报警     209807\n",
       "Name: count, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "pressure_f_r_error\n",
       "低压故障        31624\n",
       "低压红色报警          0\n",
       "低压黄色报警      12278\n",
       "正常        2733746\n",
       "高压黄色报警      32936\n",
       "高压红色报警     160168\n",
       "Name: count, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "pressure_b_l_error\n",
       "低压故障        34623\n",
       "低压红色报警      13743\n",
       "低压黄色报警      86466\n",
       "正常        2639717\n",
       "高压黄色报警       3509\n",
       "高压红色报警     192694\n",
       "Name: count, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "pressure_b_r_error\n",
       "低压故障        58090\n",
       "低压红色报警          0\n",
       "低压黄色报警      16115\n",
       "正常        2665849\n",
       "高压黄色报警        297\n",
       "高压红色报警     230401\n",
       "Name: count, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tire_pressure_related = [ 'pressure_f_l', 'pressure_f_r', 'pressure_b_l', 'pressure_b_r'] #此处可加减使用的轮胎\n",
    "\n",
    "for col in tire_pressure_related:\n",
    "    bins = [-float('inf'), 1, 780, 810, 920, 950, float('inf')] #此处可根据不同的轮胎设置不同的预警划分\n",
    "    labels = ['低压故障', '低压红色报警', '低压黄色报警', '正常', '高压黄色报警', '高压红色报警'] \n",
    "\n",
    "    dt[f'{col}_error'] = pd.cut(dt[col], bins= bins, labels=labels)\n",
    "    display(dt[f'{col}_error'].value_counts().sort_index())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "tire_cols = ['f_l_', 'f_r_', 'b_l_', 'b_r_']\n",
    "error_cols = ['temp_f_l_error', 'temp_f_r_error', 'temp_b_l_error',\n",
    "       'temp_b_r_error', 'pressure_f_l_error', 'pressure_f_r_error',\n",
    "       'pressure_b_l_error', 'pressure_b_r_error']\n",
    "for tire_col in tire_cols:\n",
    "    cols = [col for col in error_cols if re.search(tire_col, col)]\n",
    "    error_matrix = dt[cols]!='正常'\n",
    "    error_sum = error_matrix.sum(axis=1)\n",
    "    dt[f'{tire_col}error'] = np.select(\n",
    "        [error_sum >= 1],\n",
    "        [1],\n",
    "        default=0\n",
    "    )\n",
    "\n",
    "tire_error_cols = ['f_l_error', 'f_r_error', 'b_l_error','b_r_error']\n",
    "error_matrix = dt[tire_error_cols]\n",
    "error_sum = error_matrix.sum(axis=1)\n",
    "dt['car_error'] = np.select(\n",
    "    [error_sum>=3, error_sum>=1],\n",
    "    ['红色二级报警', '黄色一级报警'],\n",
    "    default='正常'\n",
    ")\n",
    "\n",
    "dt = dt.sort_values(by='time')\n",
    "dt.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # error_cols = ['temp_f_l_error', 'temp_f_r_error', 'temp_b_l_error',\n",
    "# #        'temp_b_r_error', 'pressure_f_l_error', 'pressure_f_r_error',\n",
    "# #        'pressure_b_l_error', 'pressure_b_r_error']\n",
    "# # dt['car_error'] = dt[error_cols].apply(lambda x:'红色报警' if sum(x!='正常')>= 6 else '黄色二级报警' if sum(x!='正常')>=3 else '黄色一级报警' if sum(x!='正常')>=1 else '正常', axis=1)\n",
    "\n",
    "# error_cols = ['temp_f_l_error', 'temp_f_r_error', 'temp_b_l_error',\n",
    "#        'temp_b_r_error', 'pressure_f_l_error', 'pressure_f_r_error',\n",
    "#        'pressure_b_l_error', 'pressure_b_r_error']\n",
    "# error_matrix = dt[error_cols]!='正常'\n",
    "# error_sum = error_matrix.sum(axis=1)\n",
    "# dt['car_error'] = np.select(\n",
    "#     [error_sum >= 6, error_sum >= 3, error_sum >= 1],\n",
    "#     ['红色报警','黄色二级报警', '黄色一级报警'],\n",
    "#     default='正常'\n",
    "# )\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['temp_f_l', 'temp_f_r', 'temp_m_l', 'temp_m_r', 'temp_b_l', 'temp_b_r',\n",
       "       'pressure_f_l', 'pressure_f_r', 'pressure_m_l', 'pressure_m_r',\n",
       "       'pressure_b_l', 'pressure_b_r', 'date', 'hour', 'month', 'time',\n",
       "       'vehicle_number', 'temp_f_l_error', 'temp_f_r_error', 'temp_b_l_error',\n",
       "       'temp_b_r_error', 'pressure_f_l_error', 'pressure_f_r_error',\n",
       "       'pressure_b_l_error', 'pressure_b_r_error', 'f_l_error', 'f_r_error',\n",
       "       'b_l_error', 'b_r_error', 'car_error'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>hour</th>\n",
       "      <th>time</th>\n",
       "      <th>temp_f_l_error</th>\n",
       "      <th>temp_f_l_error_duration</th>\n",
       "      <th>temp_f_r_error</th>\n",
       "      <th>temp_f_r_error_duration</th>\n",
       "      <th>temp_b_l_error</th>\n",
       "      <th>temp_b_l_error_duration</th>\n",
       "      <th>temp_b_r_error</th>\n",
       "      <th>...</th>\n",
       "      <th>pressure_f_l_error</th>\n",
       "      <th>pressure_f_l_error_duration</th>\n",
       "      <th>pressure_f_r_error</th>\n",
       "      <th>pressure_f_r_error_duration</th>\n",
       "      <th>pressure_b_l_error</th>\n",
       "      <th>pressure_b_l_error_duration</th>\n",
       "      <th>pressure_b_r_error</th>\n",
       "      <th>pressure_b_r_error_duration</th>\n",
       "      <th>car_error</th>\n",
       "      <th>car_error_duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2024-05-23</td>\n",
       "      <td>15</td>\n",
       "      <td>2024-05-23 15:27:19</td>\n",
       "      <td>正常</td>\n",
       "      <td>398.933333</td>\n",
       "      <td>正常</td>\n",
       "      <td>789.483333</td>\n",
       "      <td>高温故障</td>\n",
       "      <td>1480.116667</td>\n",
       "      <td>高温故障</td>\n",
       "      <td>...</td>\n",
       "      <td>高压黄色报警</td>\n",
       "      <td>105.433333</td>\n",
       "      <td>高压黄色报警</td>\n",
       "      <td>83.666667</td>\n",
       "      <td>低压故障</td>\n",
       "      <td>19.983333</td>\n",
       "      <td>低压故障</td>\n",
       "      <td>19.816667</td>\n",
       "      <td>红色二级报警</td>\n",
       "      <td>1508.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2024-05-23</td>\n",
       "      <td>15</td>\n",
       "      <td>2024-05-23 15:27:20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2024-05-23</td>\n",
       "      <td>15</td>\n",
       "      <td>2024-05-23 15:27:21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2024-05-23</td>\n",
       "      <td>15</td>\n",
       "      <td>2024-05-23 15:27:22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2024-05-23</td>\n",
       "      <td>15</td>\n",
       "      <td>2024-05-23 15:27:23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  hour                 time temp_f_l_error  \\\n",
       "0  2024-05-23    15  2024-05-23 15:27:19             正常   \n",
       "1  2024-05-23    15  2024-05-23 15:27:20            NaN   \n",
       "2  2024-05-23    15  2024-05-23 15:27:21            NaN   \n",
       "3  2024-05-23    15  2024-05-23 15:27:22            NaN   \n",
       "4  2024-05-23    15  2024-05-23 15:27:23            NaN   \n",
       "\n",
       "   temp_f_l_error_duration temp_f_r_error  temp_f_r_error_duration  \\\n",
       "0               398.933333             正常               789.483333   \n",
       "1                      NaN            NaN                      NaN   \n",
       "2                      NaN            NaN                      NaN   \n",
       "3                      NaN            NaN                      NaN   \n",
       "4                      NaN            NaN                      NaN   \n",
       "\n",
       "  temp_b_l_error  temp_b_l_error_duration temp_b_r_error  ...  \\\n",
       "0           高温故障              1480.116667           高温故障  ...   \n",
       "1            NaN                      NaN            NaN  ...   \n",
       "2            NaN                      NaN            NaN  ...   \n",
       "3            NaN                      NaN            NaN  ...   \n",
       "4            NaN                      NaN            NaN  ...   \n",
       "\n",
       "   pressure_f_l_error pressure_f_l_error_duration  pressure_f_r_error  \\\n",
       "0              高压黄色报警                  105.433333              高压黄色报警   \n",
       "1                 NaN                         NaN                 NaN   \n",
       "2                 NaN                         NaN                 NaN   \n",
       "3                 NaN                         NaN                 NaN   \n",
       "4                 NaN                         NaN                 NaN   \n",
       "\n",
       "  pressure_f_r_error_duration  pressure_b_l_error pressure_b_l_error_duration  \\\n",
       "0                   83.666667                低压故障                   19.983333   \n",
       "1                         NaN                 NaN                         NaN   \n",
       "2                         NaN                 NaN                         NaN   \n",
       "3                         NaN                 NaN                         NaN   \n",
       "4                         NaN                 NaN                         NaN   \n",
       "\n",
       "   pressure_b_r_error pressure_b_r_error_duration  car_error  \\\n",
       "0                低压故障                   19.816667     红色二级报警   \n",
       "1                 NaN                         NaN        NaN   \n",
       "2                 NaN                         NaN        NaN   \n",
       "3                 NaN                         NaN        NaN   \n",
       "4                 NaN                         NaN        NaN   \n",
       "\n",
       "  car_error_duration  \n",
       "0        1508.166667  \n",
       "1                NaN  \n",
       "2                NaN  \n",
       "3                NaN  \n",
       "4                NaN  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "error_cols = ['temp_f_l_error', 'temp_f_r_error', 'temp_b_l_error',\n",
    "       'temp_b_r_error', 'pressure_f_l_error', 'pressure_f_r_error',\n",
    "       'pressure_b_l_error', 'pressure_b_r_error', 'car_error']\n",
    "date_cols = ['date', 'hour', 'time']\n",
    "error_total = dt[date_cols].copy()\n",
    "# dic = dict.fromkeys(error_cols, np.nan)\n",
    "# error_total = pd.concat([error_total, pd.DataFrame(dic, index=error_total.index)], axis=1)\n",
    "\n",
    "# for i in range(1, len(error_total)):\n",
    "#     if dt[col].loc[i] == dt[col].loc[i-1]:\n",
    "#         continue\n",
    "#     else:\n",
    "#         error_total[col].loc[i] = dt[col].loc[i]\n",
    "\n",
    "\n",
    "# 使用pd.shift()实现sql lead,lag效果\n",
    "for col in error_cols:\n",
    "    result = dt[col].loc[dt[col]!=dt[col].shift()]\n",
    "    a = pd.to_datetime(error_total['time']).loc[result.index] \n",
    "    b = pd.to_datetime(error_total['time']).loc[result.index].shift(-1)\n",
    "    duration = pd.DataFrame({f'{col}_duration': (b-a) / pd.Timedelta(minutes=1)})\n",
    "    error_total = pd.merge(error_total, result, left_index=True, right_index=True, how='left')\n",
    "    error_total = pd.merge(error_total, duration, left_index=True, right_index=True, how='left')\n",
    "error_total.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 每个轮子，按月，按天的报警情况"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 按月"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "error_total['month'] = pd.to_datetime(error_total['date']).dt.month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "error_tire_cols = ['temp_f_l_error', 'temp_f_r_error', 'temp_b_l_error',\n",
    "       'temp_b_r_error', 'pressure_f_l_error', 'pressure_f_r_error',\n",
    "       'pressure_b_l_error', 'pressure_b_r_error']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "error_temp_tire_cols = ['temp_f_l_error', 'temp_f_r_error', 'temp_b_l_error',\n",
    "       'temp_b_r_error']\n",
    "\n",
    "col = error_temp_tire_cols.pop(0)\n",
    "\n",
    "error_temp_detail = error_total.groupby([ 'month', col]).agg({'time': 'count',f'{col}_duration': 'sum'}).rename(\n",
    "    columns={f'time': f'{col}_count'}).reset_index()\n",
    "\n",
    "for col in error_temp_tire_cols:\n",
    "    X = error_total.groupby([ 'month', col]).agg({'time': 'count',f'{col}_duration': 'sum'}).rename(columns={f'time': f'{col}_count'}).reset_index()\n",
    "    X.sort_values(by=f'{col}', inplace=True)\n",
    "    error_temp_detail = pd.concat([error_temp_detail, X.drop(['month'], axis=1)], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>month</th>\n",
       "      <th>temp_f_l_error</th>\n",
       "      <th>temp_f_l_error_count</th>\n",
       "      <th>temp_f_l_error_duration</th>\n",
       "      <th>temp_f_r_error</th>\n",
       "      <th>temp_f_r_error_count</th>\n",
       "      <th>temp_f_r_error_duration</th>\n",
       "      <th>temp_b_l_error</th>\n",
       "      <th>temp_b_l_error_count</th>\n",
       "      <th>temp_b_l_error_duration</th>\n",
       "      <th>temp_b_r_error</th>\n",
       "      <th>temp_b_r_error_count</th>\n",
       "      <th>temp_b_r_error_duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>低温故障</td>\n",
       "      <td>8</td>\n",
       "      <td>38.033333</td>\n",
       "      <td>低温故障</td>\n",
       "      <td>10</td>\n",
       "      <td>32.366667</td>\n",
       "      <td>低温故障</td>\n",
       "      <td>8</td>\n",
       "      <td>38.050000</td>\n",
       "      <td>低温故障</td>\n",
       "      <td>8</td>\n",
       "      <td>34.533333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>正常</td>\n",
       "      <td>20</td>\n",
       "      <td>10509.766667</td>\n",
       "      <td>正常</td>\n",
       "      <td>16</td>\n",
       "      <td>11326.966667</td>\n",
       "      <td>正常</td>\n",
       "      <td>14</td>\n",
       "      <td>12593.550000</td>\n",
       "      <td>正常</td>\n",
       "      <td>1</td>\n",
       "      <td>3487.150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>高温黄色报警</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>高温黄色报警</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>高温黄色报警</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>高温黄色报警</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>高温红色报警</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>高温红色报警</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>高温红色报警</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>高温红色报警</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>高温故障</td>\n",
       "      <td>15</td>\n",
       "      <td>3125.850000</td>\n",
       "      <td>高温故障</td>\n",
       "      <td>7</td>\n",
       "      <td>2314.333333</td>\n",
       "      <td>高温故障</td>\n",
       "      <td>6</td>\n",
       "      <td>2486.366667</td>\n",
       "      <td>高温故障</td>\n",
       "      <td>9</td>\n",
       "      <td>10152.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>低温故障</td>\n",
       "      <td>1</td>\n",
       "      <td>2.816667</td>\n",
       "      <td>低温故障</td>\n",
       "      <td>1</td>\n",
       "      <td>2.816667</td>\n",
       "      <td>低温故障</td>\n",
       "      <td>2</td>\n",
       "      <td>3.700000</td>\n",
       "      <td>低温故障</td>\n",
       "      <td>2</td>\n",
       "      <td>3.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>正常</td>\n",
       "      <td>28</td>\n",
       "      <td>41757.500000</td>\n",
       "      <td>正常</td>\n",
       "      <td>31</td>\n",
       "      <td>41697.300000</td>\n",
       "      <td>正常</td>\n",
       "      <td>86</td>\n",
       "      <td>38461.533333</td>\n",
       "      <td>正常</td>\n",
       "      <td>131</td>\n",
       "      <td>37888.983333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>6</td>\n",
       "      <td>高温黄色报警</td>\n",
       "      <td>2</td>\n",
       "      <td>9.883333</td>\n",
       "      <td>高温黄色报警</td>\n",
       "      <td>4</td>\n",
       "      <td>50.250000</td>\n",
       "      <td>高温黄色报警</td>\n",
       "      <td>64</td>\n",
       "      <td>1807.650000</td>\n",
       "      <td>高温黄色报警</td>\n",
       "      <td>133</td>\n",
       "      <td>3402.650000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>6</td>\n",
       "      <td>高温红色报警</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>高温红色报警</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>高温红色报警</td>\n",
       "      <td>3</td>\n",
       "      <td>46.200000</td>\n",
       "      <td>高温红色报警</td>\n",
       "      <td>25</td>\n",
       "      <td>461.783333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>6</td>\n",
       "      <td>高温故障</td>\n",
       "      <td>25</td>\n",
       "      <td>44.066667</td>\n",
       "      <td>高温故障</td>\n",
       "      <td>26</td>\n",
       "      <td>63.883333</td>\n",
       "      <td>高温故障</td>\n",
       "      <td>23</td>\n",
       "      <td>50.933333</td>\n",
       "      <td>高温故障</td>\n",
       "      <td>23</td>\n",
       "      <td>57.150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>7</td>\n",
       "      <td>低温故障</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>低温故障</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>低温故障</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>低温故障</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>7</td>\n",
       "      <td>正常</td>\n",
       "      <td>9</td>\n",
       "      <td>15794.283333</td>\n",
       "      <td>正常</td>\n",
       "      <td>9</td>\n",
       "      <td>15809.216667</td>\n",
       "      <td>正常</td>\n",
       "      <td>10</td>\n",
       "      <td>15790.100000</td>\n",
       "      <td>正常</td>\n",
       "      <td>24</td>\n",
       "      <td>16186.183333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>7</td>\n",
       "      <td>高温黄色报警</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>高温黄色报警</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>高温黄色报警</td>\n",
       "      <td>2</td>\n",
       "      <td>9.866667</td>\n",
       "      <td>高温黄色报警</td>\n",
       "      <td>15</td>\n",
       "      <td>173.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>7</td>\n",
       "      <td>高温红色报警</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>高温红色报警</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>高温红色报警</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>高温红色报警</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>7</td>\n",
       "      <td>高温故障</td>\n",
       "      <td>9</td>\n",
       "      <td>26.983333</td>\n",
       "      <td>高温故障</td>\n",
       "      <td>9</td>\n",
       "      <td>14.850000</td>\n",
       "      <td>高温故障</td>\n",
       "      <td>8</td>\n",
       "      <td>23.083333</td>\n",
       "      <td>高温故障</td>\n",
       "      <td>9</td>\n",
       "      <td>13.583333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    month temp_f_l_error  temp_f_l_error_count  temp_f_l_error_duration  \\\n",
       "0       5           低温故障                     8                38.033333   \n",
       "1       5             正常                    20             10509.766667   \n",
       "2       5         高温黄色报警                     0                 0.000000   \n",
       "3       5         高温红色报警                     0                 0.000000   \n",
       "4       5           高温故障                    15              3125.850000   \n",
       "5       6           低温故障                     1                 2.816667   \n",
       "6       6             正常                    28             41757.500000   \n",
       "7       6         高温黄色报警                     2                 9.883333   \n",
       "8       6         高温红色报警                     0                 0.000000   \n",
       "9       6           高温故障                    25                44.066667   \n",
       "10      7           低温故障                     0                 0.000000   \n",
       "11      7             正常                     9             15794.283333   \n",
       "12      7         高温黄色报警                     0                 0.000000   \n",
       "13      7         高温红色报警                     0                 0.000000   \n",
       "14      7           高温故障                     9                26.983333   \n",
       "\n",
       "   temp_f_r_error  temp_f_r_error_count  temp_f_r_error_duration  \\\n",
       "0            低温故障                    10                32.366667   \n",
       "1              正常                    16             11326.966667   \n",
       "2          高温黄色报警                     0                 0.000000   \n",
       "3          高温红色报警                     0                 0.000000   \n",
       "4            高温故障                     7              2314.333333   \n",
       "5            低温故障                     1                 2.816667   \n",
       "6              正常                    31             41697.300000   \n",
       "7          高温黄色报警                     4                50.250000   \n",
       "8          高温红色报警                     0                 0.000000   \n",
       "9            高温故障                    26                63.883333   \n",
       "10           低温故障                     0                 0.000000   \n",
       "11             正常                     9             15809.216667   \n",
       "12         高温黄色报警                     0                 0.000000   \n",
       "13         高温红色报警                     0                 0.000000   \n",
       "14           高温故障                     9                14.850000   \n",
       "\n",
       "   temp_b_l_error  temp_b_l_error_count  temp_b_l_error_duration  \\\n",
       "0            低温故障                     8                38.050000   \n",
       "1              正常                    14             12593.550000   \n",
       "2          高温黄色报警                     0                 0.000000   \n",
       "3          高温红色报警                     0                 0.000000   \n",
       "4            高温故障                     6              2486.366667   \n",
       "5            低温故障                     2                 3.700000   \n",
       "6              正常                    86             38461.533333   \n",
       "7          高温黄色报警                    64              1807.650000   \n",
       "8          高温红色报警                     3                46.200000   \n",
       "9            高温故障                    23                50.933333   \n",
       "10           低温故障                     0                 0.000000   \n",
       "11             正常                    10             15790.100000   \n",
       "12         高温黄色报警                     2                 9.866667   \n",
       "13         高温红色报警                     0                 0.000000   \n",
       "14           高温故障                     8                23.083333   \n",
       "\n",
       "   temp_b_r_error  temp_b_r_error_count  temp_b_r_error_duration  \n",
       "0            低温故障                     8                34.533333  \n",
       "1              正常                     1              3487.150000  \n",
       "2          高温黄色报警                     0                 0.000000  \n",
       "3          高温红色报警                     0                 0.000000  \n",
       "4            高温故障                     9             10152.050000  \n",
       "5            低温故障                     2                 3.700000  \n",
       "6              正常                   131             37888.983333  \n",
       "7          高温黄色报警                   133              3402.650000  \n",
       "8          高温红色报警                    25               461.783333  \n",
       "9            高温故障                    23                57.150000  \n",
       "10           低温故障                     0                 0.000000  \n",
       "11             正常                    24             16186.183333  \n",
       "12         高温黄色报警                    15               173.900000  \n",
       "13         高温红色报警                     0                 0.000000  \n",
       "14           高温故障                     9                13.583333  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "error_temp_detail"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "error_pressure_tire_cols = ['pressure_f_l_error', 'pressure_f_r_error',\n",
    "       'pressure_b_l_error', 'pressure_b_r_error']\n",
    "\n",
    "col = error_pressure_tire_cols.pop(0)\n",
    "\n",
    "error_pressure_detail = error_total.groupby([ 'month', col]).agg({'time': 'count',f'{col}_duration': 'sum'}).rename(\n",
    "    columns={f'time': f'{col}_count'}).reset_index()\n",
    "\n",
    "for col in error_pressure_tire_cols:\n",
    "    X = error_total.groupby([ 'month', col]).agg({'time': 'count',f'{col}_duration': 'sum'}).rename(columns={f'time': f'{col}_count'}).reset_index()\n",
    "    X.sort_values(by=f'{col}', inplace=True)\n",
    "    error_pressure_detail = pd.concat([error_pressure_detail, X.drop(['month'], axis=1)], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>month</th>\n",
       "      <th>pressure_f_l_error</th>\n",
       "      <th>pressure_f_l_error_count</th>\n",
       "      <th>pressure_f_l_error_duration</th>\n",
       "      <th>pressure_f_r_error</th>\n",
       "      <th>pressure_f_r_error_count</th>\n",
       "      <th>pressure_f_r_error_duration</th>\n",
       "      <th>pressure_b_l_error</th>\n",
       "      <th>pressure_b_l_error_count</th>\n",
       "      <th>pressure_b_l_error_duration</th>\n",
       "      <th>pressure_b_r_error</th>\n",
       "      <th>pressure_b_r_error_count</th>\n",
       "      <th>pressure_b_r_error_duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>低压故障</td>\n",
       "      <td>21</td>\n",
       "      <td>687.300000</td>\n",
       "      <td>低压故障</td>\n",
       "      <td>17</td>\n",
       "      <td>481.933333</td>\n",
       "      <td>低压故障</td>\n",
       "      <td>19</td>\n",
       "      <td>538.850000</td>\n",
       "      <td>低压故障</td>\n",
       "      <td>21</td>\n",
       "      <td>1049.850000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>低压红色报警</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>低压红色报警</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>低压红色报警</td>\n",
       "      <td>1</td>\n",
       "      <td>324.716667</td>\n",
       "      <td>低压红色报警</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>低压黄色报警</td>\n",
       "      <td>1</td>\n",
       "      <td>412.150000</td>\n",
       "      <td>低压黄色报警</td>\n",
       "      <td>1</td>\n",
       "      <td>194.516667</td>\n",
       "      <td>低压黄色报警</td>\n",
       "      <td>2</td>\n",
       "      <td>1259.866667</td>\n",
       "      <td>低压黄色报警</td>\n",
       "      <td>1</td>\n",
       "      <td>324.716667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>正常</td>\n",
       "      <td>11</td>\n",
       "      <td>2283.383333</td>\n",
       "      <td>正常</td>\n",
       "      <td>27</td>\n",
       "      <td>4525.300000</td>\n",
       "      <td>正常</td>\n",
       "      <td>23</td>\n",
       "      <td>4918.633333</td>\n",
       "      <td>正常</td>\n",
       "      <td>2</td>\n",
       "      <td>2171.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>高压黄色报警</td>\n",
       "      <td>9</td>\n",
       "      <td>1354.100000</td>\n",
       "      <td>高压黄色报警</td>\n",
       "      <td>9</td>\n",
       "      <td>753.600000</td>\n",
       "      <td>高压黄色报警</td>\n",
       "      <td>1</td>\n",
       "      <td>63.933333</td>\n",
       "      <td>高压黄色报警</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>高压红色报警</td>\n",
       "      <td>19</td>\n",
       "      <td>7945.466667</td>\n",
       "      <td>高压红色报警</td>\n",
       "      <td>27</td>\n",
       "      <td>6727.050000</td>\n",
       "      <td>高压红色报警</td>\n",
       "      <td>21</td>\n",
       "      <td>5576.400000</td>\n",
       "      <td>高压红色报警</td>\n",
       "      <td>21</td>\n",
       "      <td>9136.733333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>低压故障</td>\n",
       "      <td>26</td>\n",
       "      <td>46.883333</td>\n",
       "      <td>低压故障</td>\n",
       "      <td>27</td>\n",
       "      <td>66.700000</td>\n",
       "      <td>低压故障</td>\n",
       "      <td>25</td>\n",
       "      <td>54.633333</td>\n",
       "      <td>低压故障</td>\n",
       "      <td>25</td>\n",
       "      <td>60.850000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>6</td>\n",
       "      <td>低压红色报警</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>低压红色报警</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>低压红色报警</td>\n",
       "      <td>2</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>低压红色报警</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>6</td>\n",
       "      <td>低压黄色报警</td>\n",
       "      <td>4</td>\n",
       "      <td>465.350000</td>\n",
       "      <td>低压黄色报警</td>\n",
       "      <td>2</td>\n",
       "      <td>104.716667</td>\n",
       "      <td>低压黄色报警</td>\n",
       "      <td>9</td>\n",
       "      <td>1325.583333</td>\n",
       "      <td>低压黄色报警</td>\n",
       "      <td>1</td>\n",
       "      <td>50.616667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>6</td>\n",
       "      <td>正常</td>\n",
       "      <td>30</td>\n",
       "      <td>42293.283333</td>\n",
       "      <td>正常</td>\n",
       "      <td>31</td>\n",
       "      <td>42619.066667</td>\n",
       "      <td>正常</td>\n",
       "      <td>34</td>\n",
       "      <td>41356.350000</td>\n",
       "      <td>正常</td>\n",
       "      <td>29</td>\n",
       "      <td>42689.183333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>6</td>\n",
       "      <td>高压黄色报警</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>高压黄色报警</td>\n",
       "      <td>2</td>\n",
       "      <td>15.033333</td>\n",
       "      <td>高压黄色报警</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>高压黄色报警</td>\n",
       "      <td>3</td>\n",
       "      <td>4.950000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>6</td>\n",
       "      <td>高压红色报警</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>高压红色报警</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>高压红色报警</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>高压红色报警</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>7</td>\n",
       "      <td>低压故障</td>\n",
       "      <td>9</td>\n",
       "      <td>26.983333</td>\n",
       "      <td>低压故障</td>\n",
       "      <td>9</td>\n",
       "      <td>14.850000</td>\n",
       "      <td>低压故障</td>\n",
       "      <td>8</td>\n",
       "      <td>23.100000</td>\n",
       "      <td>低压故障</td>\n",
       "      <td>9</td>\n",
       "      <td>13.583333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>7</td>\n",
       "      <td>低压红色报警</td>\n",
       "      <td>2</td>\n",
       "      <td>6.600000</td>\n",
       "      <td>低压红色报警</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>低压红色报警</td>\n",
       "      <td>3</td>\n",
       "      <td>468.950000</td>\n",
       "      <td>低压红色报警</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>7</td>\n",
       "      <td>低压黄色报警</td>\n",
       "      <td>9</td>\n",
       "      <td>1515.733333</td>\n",
       "      <td>低压黄色报警</td>\n",
       "      <td>3</td>\n",
       "      <td>431.350000</td>\n",
       "      <td>低压黄色报警</td>\n",
       "      <td>15</td>\n",
       "      <td>1903.600000</td>\n",
       "      <td>低压黄色报警</td>\n",
       "      <td>5</td>\n",
       "      <td>599.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>7</td>\n",
       "      <td>正常</td>\n",
       "      <td>16</td>\n",
       "      <td>14271.950000</td>\n",
       "      <td>正常</td>\n",
       "      <td>12</td>\n",
       "      <td>15377.866667</td>\n",
       "      <td>正常</td>\n",
       "      <td>20</td>\n",
       "      <td>13427.416667</td>\n",
       "      <td>正常</td>\n",
       "      <td>12</td>\n",
       "      <td>15208.616667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>7</td>\n",
       "      <td>高压黄色报警</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>高压黄色报警</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>高压黄色报警</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>高压黄色报警</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>7</td>\n",
       "      <td>高压红色报警</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>高压红色报警</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>高压红色报警</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>高压红色报警</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    month pressure_f_l_error  pressure_f_l_error_count  \\\n",
       "0       5               低压故障                        21   \n",
       "1       5             低压红色报警                         0   \n",
       "2       5             低压黄色报警                         1   \n",
       "3       5                 正常                        11   \n",
       "4       5             高压黄色报警                         9   \n",
       "5       5             高压红色报警                        19   \n",
       "6       6               低压故障                        26   \n",
       "7       6             低压红色报警                         0   \n",
       "8       6             低压黄色报警                         4   \n",
       "9       6                 正常                        30   \n",
       "10      6             高压黄色报警                         0   \n",
       "11      6             高压红色报警                         0   \n",
       "12      7               低压故障                         9   \n",
       "13      7             低压红色报警                         2   \n",
       "14      7             低压黄色报警                         9   \n",
       "15      7                 正常                        16   \n",
       "16      7             高压黄色报警                         0   \n",
       "17      7             高压红色报警                         0   \n",
       "\n",
       "    pressure_f_l_error_duration pressure_f_r_error  pressure_f_r_error_count  \\\n",
       "0                    687.300000               低压故障                        17   \n",
       "1                      0.000000             低压红色报警                         0   \n",
       "2                    412.150000             低压黄色报警                         1   \n",
       "3                   2283.383333                 正常                        27   \n",
       "4                   1354.100000             高压黄色报警                         9   \n",
       "5                   7945.466667             高压红色报警                        27   \n",
       "6                     46.883333               低压故障                        27   \n",
       "7                      0.000000             低压红色报警                         0   \n",
       "8                    465.350000             低压黄色报警                         2   \n",
       "9                  42293.283333                 正常                        31   \n",
       "10                     0.000000             高压黄色报警                         2   \n",
       "11                     0.000000             高压红色报警                         0   \n",
       "12                    26.983333               低压故障                         9   \n",
       "13                     6.600000             低压红色报警                         0   \n",
       "14                  1515.733333             低压黄色报警                         3   \n",
       "15                 14271.950000                 正常                        12   \n",
       "16                     0.000000             高压黄色报警                         0   \n",
       "17                     0.000000             高压红色报警                         0   \n",
       "\n",
       "    pressure_f_r_error_duration pressure_b_l_error  pressure_b_l_error_count  \\\n",
       "0                    481.933333               低压故障                        19   \n",
       "1                      0.000000             低压红色报警                         1   \n",
       "2                    194.516667             低压黄色报警                         2   \n",
       "3                   4525.300000                 正常                        23   \n",
       "4                    753.600000             高压黄色报警                         1   \n",
       "5                   6727.050000             高压红色报警                        21   \n",
       "6                     66.700000               低压故障                        25   \n",
       "7                      0.000000             低压红色报警                         2   \n",
       "8                    104.716667             低压黄色报警                         9   \n",
       "9                  42619.066667                 正常                        34   \n",
       "10                    15.033333             高压黄色报警                         0   \n",
       "11                     0.000000             高压红色报警                         0   \n",
       "12                    14.850000               低压故障                         8   \n",
       "13                     0.000000             低压红色报警                         3   \n",
       "14                   431.350000             低压黄色报警                        15   \n",
       "15                 15377.866667                 正常                        20   \n",
       "16                     0.000000             高压黄色报警                         0   \n",
       "17                     0.000000             高压红色报警                         0   \n",
       "\n",
       "    pressure_b_l_error_duration pressure_b_r_error  pressure_b_r_error_count  \\\n",
       "0                    538.850000               低压故障                        21   \n",
       "1                    324.716667             低压红色报警                         0   \n",
       "2                   1259.866667             低压黄色报警                         1   \n",
       "3                   4918.633333                 正常                         2   \n",
       "4                     63.933333             高压黄色报警                         0   \n",
       "5                   5576.400000             高压红色报警                        21   \n",
       "6                     54.633333               低压故障                        25   \n",
       "7                     69.000000             低压红色报警                         0   \n",
       "8                   1325.583333             低压黄色报警                         1   \n",
       "9                  41356.350000                 正常                        29   \n",
       "10                     0.000000             高压黄色报警                         3   \n",
       "11                     0.000000             高压红色报警                         0   \n",
       "12                    23.100000               低压故障                         9   \n",
       "13                   468.950000             低压红色报警                         0   \n",
       "14                  1903.600000             低压黄色报警                         5   \n",
       "15                 13427.416667                 正常                        12   \n",
       "16                     0.000000             高压黄色报警                         0   \n",
       "17                     0.000000             高压红色报警                         0   \n",
       "\n",
       "    pressure_b_r_error_duration  \n",
       "0                   1049.850000  \n",
       "1                      0.000000  \n",
       "2                    324.716667  \n",
       "3                   2171.100000  \n",
       "4                      0.000000  \n",
       "5                   9136.733333  \n",
       "6                     60.850000  \n",
       "7                      0.000000  \n",
       "8                     50.616667  \n",
       "9                  42689.183333  \n",
       "10                     4.950000  \n",
       "11                     0.000000  \n",
       "12                    13.583333  \n",
       "13                     0.000000  \n",
       "14                   599.000000  \n",
       "15                 15208.616667  \n",
       "16                     0.000000  \n",
       "17                     0.000000  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "error_pressure_detail"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "col = 'car_error'\n",
    "error_car_detail = error_total.groupby([ 'month', col]).agg({'time': 'count',f'{col}_duration': 'sum'}).rename(\n",
    "    columns={f'time': f'{col}_count'}).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "error_pressure_detail.to_csv('M_error_pressure_detail.csv', encoding='utf_8_sig')\n",
    "error_temp_detail.to_csv('M_error_temp_detail.csv', encoding='utf_8_sig')\n",
    "error_car_detail.to_csv('M_error_car_detail.csv', encoding='utf_8_sig')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 按天"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "error_tire_cols = ['temp_f_l_error', 'temp_f_r_error', 'temp_b_l_error',\n",
    "       'temp_b_r_error', 'pressure_f_l_error', 'pressure_f_r_error',\n",
    "       'pressure_b_l_error', 'pressure_b_r_error']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "error_temp_tire_cols = ['temp_f_l_error', 'temp_f_r_error', 'temp_b_l_error',\n",
    "       'temp_b_r_error']\n",
    "\n",
    "col = error_temp_tire_cols.pop(0)\n",
    "\n",
    "error_temp_detail = error_total.groupby([ 'date', col]).agg({'time': 'count',f'{col}_duration': 'sum'}).rename(\n",
    "    columns={f'time': f'{col}_count'}).reset_index()\n",
    "\n",
    "for col in error_temp_tire_cols:\n",
    "    X = error_total.groupby([ 'date', col]).agg({'time': 'count',f'{col}_duration': 'sum'}).rename(columns={f'time': f'{col}_count'}).reset_index()\n",
    "    X.sort_values(by=f'{col}', inplace=True)\n",
    "    error_temp_detail = pd.concat([error_temp_detail, X.drop(['date'], axis=1)], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>temp_f_l_error</th>\n",
       "      <th>temp_f_l_error_count</th>\n",
       "      <th>temp_f_l_error_duration</th>\n",
       "      <th>temp_f_r_error</th>\n",
       "      <th>temp_f_r_error_count</th>\n",
       "      <th>temp_f_r_error_duration</th>\n",
       "      <th>temp_b_l_error</th>\n",
       "      <th>temp_b_l_error_count</th>\n",
       "      <th>temp_b_l_error_duration</th>\n",
       "      <th>temp_b_r_error</th>\n",
       "      <th>temp_b_r_error_count</th>\n",
       "      <th>temp_b_r_error_duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2024-05-23</td>\n",
       "      <td>低温故障</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>低温故障</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>低温故障</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>低温故障</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2024-05-23</td>\n",
       "      <td>正常</td>\n",
       "      <td>2</td>\n",
       "      <td>703.250000</td>\n",
       "      <td>正常</td>\n",
       "      <td>1</td>\n",
       "      <td>789.483333</td>\n",
       "      <td>正常</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>正常</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2024-05-23</td>\n",
       "      <td>高温黄色报警</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>高温黄色报警</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>高温黄色报警</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>高温黄色报警</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2024-05-23</td>\n",
       "      <td>高温红色报警</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>高温红色报警</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>高温红色报警</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>高温红色报警</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2024-05-23</td>\n",
       "      <td>高温故障</td>\n",
       "      <td>1</td>\n",
       "      <td>86.233333</td>\n",
       "      <td>高温故障</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>高温故障</td>\n",
       "      <td>1</td>\n",
       "      <td>1480.116667</td>\n",
       "      <td>高温故障</td>\n",
       "      <td>1</td>\n",
       "      <td>5766.550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>2024-07-12</td>\n",
       "      <td>低温故障</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>低温故障</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>低温故障</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>低温故障</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>2024-07-12</td>\n",
       "      <td>正常</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>正常</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>正常</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>正常</td>\n",
       "      <td>2</td>\n",
       "      <td>547.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252</th>\n",
       "      <td>2024-07-12</td>\n",
       "      <td>高温黄色报警</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>高温黄色报警</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>高温黄色报警</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>高温黄色报警</td>\n",
       "      <td>1</td>\n",
       "      <td>4.966667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>253</th>\n",
       "      <td>2024-07-12</td>\n",
       "      <td>高温红色报警</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>高温红色报警</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>高温红色报警</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>高温红色报警</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>254</th>\n",
       "      <td>2024-07-12</td>\n",
       "      <td>高温故障</td>\n",
       "      <td>1</td>\n",
       "      <td>1.116667</td>\n",
       "      <td>高温故障</td>\n",
       "      <td>1</td>\n",
       "      <td>3.916667</td>\n",
       "      <td>高温故障</td>\n",
       "      <td>1</td>\n",
       "      <td>2.933333</td>\n",
       "      <td>高温故障</td>\n",
       "      <td>1</td>\n",
       "      <td>1.050000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>255 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           date temp_f_l_error  temp_f_l_error_count  temp_f_l_error_duration  \\\n",
       "0    2024-05-23           低温故障                     0                 0.000000   \n",
       "1    2024-05-23             正常                     2               703.250000   \n",
       "2    2024-05-23         高温黄色报警                     0                 0.000000   \n",
       "3    2024-05-23         高温红色报警                     0                 0.000000   \n",
       "4    2024-05-23           高温故障                     1                86.233333   \n",
       "..          ...            ...                   ...                      ...   \n",
       "250  2024-07-12           低温故障                     0                 0.000000   \n",
       "251  2024-07-12             正常                     1                 0.000000   \n",
       "252  2024-07-12         高温黄色报警                     0                 0.000000   \n",
       "253  2024-07-12         高温红色报警                     0                 0.000000   \n",
       "254  2024-07-12           高温故障                     1                 1.116667   \n",
       "\n",
       "    temp_f_r_error  temp_f_r_error_count  temp_f_r_error_duration  \\\n",
       "0             低温故障                     0                 0.000000   \n",
       "1               正常                     1               789.483333   \n",
       "2           高温黄色报警                     0                 0.000000   \n",
       "3           高温红色报警                     0                 0.000000   \n",
       "4             高温故障                     0                 0.000000   \n",
       "..             ...                   ...                      ...   \n",
       "250           低温故障                     0                 0.000000   \n",
       "251             正常                     1                 0.000000   \n",
       "252         高温黄色报警                     0                 0.000000   \n",
       "253         高温红色报警                     0                 0.000000   \n",
       "254           高温故障                     1                 3.916667   \n",
       "\n",
       "    temp_b_l_error  temp_b_l_error_count  temp_b_l_error_duration  \\\n",
       "0             低温故障                     0                 0.000000   \n",
       "1               正常                     0                 0.000000   \n",
       "2           高温黄色报警                     0                 0.000000   \n",
       "3           高温红色报警                     0                 0.000000   \n",
       "4             高温故障                     1              1480.116667   \n",
       "..             ...                   ...                      ...   \n",
       "250           低温故障                     0                 0.000000   \n",
       "251             正常                     1                 0.000000   \n",
       "252         高温黄色报警                     0                 0.000000   \n",
       "253         高温红色报警                     0                 0.000000   \n",
       "254           高温故障                     1                 2.933333   \n",
       "\n",
       "    temp_b_r_error  temp_b_r_error_count  temp_b_r_error_duration  \n",
       "0             低温故障                     0                 0.000000  \n",
       "1               正常                     0                 0.000000  \n",
       "2           高温黄色报警                     0                 0.000000  \n",
       "3           高温红色报警                     0                 0.000000  \n",
       "4             高温故障                     1              5766.550000  \n",
       "..             ...                   ...                      ...  \n",
       "250           低温故障                     0                 0.000000  \n",
       "251             正常                     2               547.500000  \n",
       "252         高温黄色报警                     1                 4.966667  \n",
       "253         高温红色报警                     0                 0.000000  \n",
       "254           高温故障                     1                 1.050000  \n",
       "\n",
       "[255 rows x 13 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "error_temp_detail"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "error_pressure_tire_cols = ['pressure_f_l_error', 'pressure_f_r_error',\n",
    "       'pressure_b_l_error', 'pressure_b_r_error']\n",
    "\n",
    "col = error_pressure_tire_cols.pop(0)\n",
    "\n",
    "error_pressure_detail = error_total.groupby([ 'date', col]).agg({'time': 'count',f'{col}_duration': 'sum'}).rename(\n",
    "    columns={f'time': f'{col}_count'}).reset_index()\n",
    "\n",
    "for col in error_pressure_tire_cols:\n",
    "    X = error_total.groupby([ 'date', col]).agg({'time': 'count',f'{col}_duration': 'sum'}).rename(columns={f'time': f'{col}_count'}).reset_index()\n",
    "    X.sort_values(by=f'{col}', inplace=True)\n",
    "    error_pressure_detail = pd.concat([error_pressure_detail, X.drop(['date'], axis=1)], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "col = 'car_error'\n",
    "error_car_detail = error_total.groupby([ 'date', col]).agg({'time': 'count',f'{col}_duration': 'sum'}).rename(\n",
    "    columns={f'time': f'{col}_count'}).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "error_pressure_detail.to_csv('D_error_pressure_detail.csv', encoding='utf_8_sig')\n",
    "error_temp_detail.to_csv('D_error_temp_detail.csv', encoding='utf_8_sig')\n",
    "error_car_detail.to_csv('D_error_car_detail.csv', encoding='utf_8_sig')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "error_total.to_csv('output\\error_total.csv', encoding='utf_8_sig')"
   ]
  }
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